No longer endemic to Africa: Kribiodosis Kieffer, 1921 (Diptera, Chironomidae) new to Oriental China with a phylogeny and expanded adult generic diagnoses

Kribiodosis Kieffer, 1921, an African genus of Chironomini (Diptera: Chironomidae), is newly recorded from the Oriental region through a new species K. cantonensis sp. n. Detailed descriptions of the male, female and a DNA barcode are provided. With the inclusion of the new species bearing scutal tubercle and fused tibial comb, the generic diagnosis needs revision and expansion. The phylogenetic position of Kribiodosis within the tribe Chironomini is explored based on five concatenated genetic makers (18S, 28S, CAD1, CAD4 and COI-3P) using both mixed-model Bayesian inference and maximum likelihood methods. Kribiodosis is placed as a core member of the Microtendipes group but its precise sister group remains unclear. Inclusion of the analysis of Nilodosis Kieffer, another Chironomini genus with an African-Oriental distribution, reveals an unexpected robust position as sister to a large and diverse inclusive group of many Chironomini.

Phylogeny of Maleae (Rosaceae) Based on Complete Chloroplast Genomes Supports the Distinction of Aria, Chamaemespilus and Torminalis as Separate Genera, Different from Sorbus sp.

Several genera formerly contained within the genus Sorbus L. sensu lato have been proposed as separate taxa, including Aria, Chamaemespilus and Torminalis. However, molecular evidence for such distinctions are rather scarce. We assembled the complete chloroplast genome of Sorbus aucuparia, another representative of Sorbus s.s., and performed detailed comparisons with the available genomes of Aria edulis, Chamaemespilus alpina and Torminalis glaberrima. Additionally, using 110 complete chloroplast genomes of the Maleae representatives, we constructed the phylogenetic tree of the tribe using Maximum Likelihood methods. The chloroplast genome of S. aucuparia was found to be similar to other species within Maleae. The phylogenetic tree of the Maleae tribe indicated that A. edulis, C. alpina and T. glaberrima formed a concise group belonging to a different clade (related to Malus) than the one including Sorbus s.s. (related to Pyrus). However, Aria and Chamaemespilus appeared to be more closely related to each other than to Torminalis. Our results provide additional support for considering Aria, Chamaemespilus and Torminalis as separate genera different from Sorbus s.s.

Robust Methods for Analysing Quantitative Trait Loci

<p>The advent of new technology for extracting genetic information from tissue samples has increased the availability of suitable data for finding genes controlling complex traits in plants, animals and humans. Quantitative trait locus (QTL) analysis relies on statistical methods to interpret genetic data in the presence of phenotype data and possibly other factors such as environmental factors. The goal is to both detect the presence of QTL with significant effects on trait value as well as to estimate their locations on the genome relative to those of known markers. This thesis reviews commonly used statistical techniques for QTL mapping in experimental populations. Regression and likelihood methods are discussed. The mixture-modelling approach to QTL mapping is explored in some detail. This thesis presents new matrix formulas for exact and convenient calculation of both the Observed and Fisher information matrices in the context of Multinomial mixtures of Univariate Normal distributions. An extension to Composite Interval mapping is proposed, together with a hypothesis testing strategy which is robust enough to de- tect existing QTL in the presence of slight deviations from model assumptions while reducing false detections.</p>

Robust Methods for Analysing Quantitative Trait Loci

<p>The advent of new technology for extracting genetic information from tissue samples has increased the availability of suitable data for finding genes controlling complex traits in plants, animals and humans. Quantitative trait locus (QTL) analysis relies on statistical methods to interpret genetic data in the presence of phenotype data and possibly other factors such as environmental factors. The goal is to both detect the presence of QTL with significant effects on trait value as well as to estimate their locations on the genome relative to those of known markers. This thesis reviews commonly used statistical techniques for QTL mapping in experimental populations. Regression and likelihood methods are discussed. The mixture-modelling approach to QTL mapping is explored in some detail. This thesis presents new matrix formulas for exact and convenient calculation of both the Observed and Fisher information matrices in the context of Multinomial mixtures of Univariate Normal distributions. An extension to Composite Interval mapping is proposed, together with a hypothesis testing strategy which is robust enough to de- tect existing QTL in the presence of slight deviations from model assumptions while reducing false detections.</p>

Cross-Validation, Information Theory, or Maximum Likelihood? A Comparison of Tuning Methods for Penalized Splines

Functional data analysis techniques, such as penalized splines, have become common tools used in a variety of applied research settings. Penalized spline estimators are frequently used in applied research to estimate unknown functions from noisy data. The success of these estimators depends on choosing a tuning parameter that provides the correct balance between fitting and smoothing the data. Several different smoothing parameter selection methods have been proposed for choosing a reasonable tuning parameter. The proposed methods generally fall into one of three categories: cross-validation methods, information theoretic methods, or maximum likelihood methods. Despite the well-known importance of selecting an ideal smoothing parameter, there is little agreement in the literature regarding which method(s) should be considered when analyzing real data. In this paper, we address this issue by exploring the practical performance of six popular tuning methods under a variety of simulated and real data situations. Our results reveal that maximum likelihood methods outperform the popular cross-validation methods in most situations—especially in the presence of correlated errors. Furthermore, our results reveal that the maximum likelihood methods perform well even when the errors are non-Gaussian and/or heteroscedastic. For real data applications, we recommend comparing results using cross-validation and maximum likelihood tuning methods, given that these methods tend to perform similarly (differently) when the model is correctly (incorrectly) specified.